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An inbred strain of plants has a mean height of 24cm. A second strain of the same species also has a mean height of 24cm. When these plants are crossed the F1 are also 24cm in height.However, when the F1 are crossed the F2 plants show a wide range of heights, the greatest number are similar to P1 and F1 but apprx. 4 out of 1000 are only 12cm tall and 4 of 1000 are 40cm tall. What fraction of the F2 plants will be apprx. 28cm in height ?
The answer given is c).
From the source that Amory cites :
You know that the number of offspring is shown by Pascal's triangle, right?
For two alleles you have 1 : 2 : 1
For three alleles 1 : 3 : 3 : 1
For four 1 : 4 : 6 : 4 : 1 etc.
So, if 4 in 1000 are 12 and 36 cm tall, that means that every in 250 plants is tall like that. And on which level of Pascal's triangle you have sum of 250? For two alleles: 1 + 2 + 1 = 4 For threee: 1 + 3 + 3 + 1 = 8 For four: 1 + 4 + 6 + 4 + 1 = 16 etc. until 1 : 8 : 28 : 56 : 70 : 56 : 28 : 8 : 1 Here you see that you have 9 options of height. The difference is 36 cm - 12 cm = 24 cm. 24 cm /9 = 3 cm, so 1 plant will 12 cm, 8 plants will be 15 cm, 28 plants will be 18 cm, 56 plants will be 21 cm, 70 plants will be 24 cm (i.e. "the majority of F2 are like P1 and F1") and 56 plants will be 27 cm tall out of 250 plants. Thus C is the closest.
Pressures of development, particularly in tropical countries, are causing an alarming increase in the rate of species extinction, making the current resurgence in systematics especially timely. Given the reasonable estimate that systematists have only discovered and named perhaps 10% of the species on earth, and the fact that only a tiny fraction of those species have been studied in any detail, there is much work to be done in a short time. Many species will go extinct before we even know them it is no wonder that systematists feel as though they are watching a huge, diverse library burn down before a card catalog has been prepared (or before anyone has read even 1% of the books!). Newly developed methods for data gathering and analysis of phylogenetic relationships position us on the threshold of a deep understanding of the history of the biological world. Loss of biological diversity is thus a disaster, both from an economic standpoint (How many organisms useful for food, medicine, or technology will go extinct?) and from a broader intellectual standpoint (How did the diversity of species come to be the way it is?).
Systematists must have technical skills to extract information at all levels of inquiry (e.g., morphology, cytology, genetics, DNA sequences, organic chemistry, anatomy, ecology) and the theoretical background to interpret it correctly. Modern biological systematics integrates a diverse array of disciplines ranging from molecular, cell and developmental biology, to ecology and evolutionary biology. Data-gathering techniques include DNA sequencing, protein electrophoresis, electron and light microscopy, controlled growth experiments, and field studies of ecology and distribution. Analytical methods are computer intensive: hardware such as digitizing tablets and video cameras are used for automated description of morphology (morphometrics), multivariate statistics are used to describe and compare species and other taxa, numerical cladistic programs are used for phylogeny reconstruction.
Specialists are needed in all groups of plants, flowering plants as well as the less heavily studied algae, mosses, ferns and fungi (including lichens). This concentration is appropriate for students planning graduate studies in these areas. More immediate employment possibilities are in the National Park Service, state and natural heritage and endangered plant programs, private consulting firms, conservation organizations, botanic gardens, and herbaria. Systematic biology is a good way to indulge urges to travel, do science, and contribute to society, all at the same time.
Students fulfilling the requirements of the Concentration in Plant Biology will receive a note on their official transcript.
A reeking, parasitic plant lost its body and much of its genetic blueprint
Sapria himalayana, a native to Southeast Asia, is an endoparasite, living inside its vine host for years before emerging as a speckled flower that can measure 20 centimeters across.
C. Davis/Harvard University
February 10, 2021 at 6:00 am
For most of their lives, plants in the Sapria genus are barely anything — thin ribbons of parasitic cells winding inside vines in Southeast Asian rainforests. They become visible only when they reproduce, bursting from their host as a dinner plate–sized flower that smells like rotting flesh.
Now, new research on the genetic instruction book of this rare plant reveals the lengths to which it has gone to become a specialized parasite. The findings, published January 22 in Current Biology, suggest that at least one species of Sapria has lost nearly half of the genes commonly found in other flowering plants and stolen many others directly from its hosts.
The plant’s rewired genetics echo its bizarre biology. Sapria and its relatives in the family Rafflesiaceae have discarded their stems, roots and any photosynthetic tissue.
“If you’re out in the forest in Borneo and these [plants] aren’t producing flowers, you’re never even going to know they’re there,” says Charles Davis, an evolutionary biologist at Harvard University.
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For years, Davis has been studying the evolution of this group of otherworldly parasites, which includes the largest flower in the world, Rafflesia arnoldii (SN: 1/10/07). When some genetic data showed a close relationship between these parasites and their vine hosts, Davis suspected horizontal gene transfer. That’s where genes move directly from one species to another — in this case, from host to parasite. But no one had yet deciphered the genome — the full genetic instruction book — for these plants.
So Davis and his team sequenced many millions of pieces of Sapria himalayana’s genome, assembling them into a cohesive picture. When the team analyzed the genome, they found an abundance of oddities.
About 44 percent of the genes found in most flowering plants were missing in S. himalayana. Yet, at the same time, the genome is about 55,000 genes long, more than that of some other nonparasitic plants. The count is inflated by many repeating segments of DNA, the team found.
Loss of the chlorophyll pigments responsible for photosynthesis is common in parasitic plants that rely on their hosts for sustenance. But S. himalayana appears to have even scrapped all genetic remnants of its chloroplasts, the cellular structures where photosynthesis occurs.
Chloroplasts have their own genome, distinct from the nuclear genome that runs a plant’s cells and the mitochondria that produce energy for the cells. S. himalayana seems to have lost this genome altogether, suggesting that the plant has purged the last remnants of its ancestral life that allowed it to make its own food.
“There is no other case” of an abandoned chloroplast genome among plants, says Davis. Earlier work by other researchers had suggested that the genome may be missing. “Our work clearly verifies that indeed it’s totally gone,” he says, noting that even genes in S. himalayana’s nuclear genome that would regulate components of the chloroplast genome have vanished.
It may be too early to declare the chloroplast genome completely missing in action, cautions Alex Twyford, an evolutionary biologist at the University of Edinburgh who was not involved with this research. It may be difficult to definitively prove the genome is gone, he says, especially if the chloroplast is “unusual in its structure or abundance” and therefore difficult to identify.
Among the remaining parts of the nuclear genome, the team also found that more than 1 percent of S. himalayana’s genome comes from genes stolen from other plants, likely its current and ancestral hosts.
The potential scale of the vanished genome and the volume of repeating bits of DNA are “insane,” says Arjan Banerjee, a biologist at the University of Toronto Mississauga also not involved with this study. The “industrial scale” of the plant’s gene theft is also impressive, he says.
There are still plenty of weird elements left in S. himalayana’s genome to explore, says study coauthor Tim Sackton, an evolutionary biologist also at Harvard. For example, the plant has bloated its genome with extraneous DNA, while most parasites streamline their genomes. “There’s something weird and different going on in this species,” he says, adding that many of the DNA fragments the parasitic plant is stealing from its host don’t appear to encode any genes, and likely don’t do anything important.
The new discovery illustrates the level of commitment S. himalayana and its relatives have given to evolving a parasitic lifestyle, and provide a comparison to other extreme plant parasites (SN: 7/31/20). And for Davis, plants like S. himalayana can help researchers determine some of biology’s limits. These plants have lost half their genes, yet they still survive, he notes. “Maybe these organisms that stretch the boundaries of existence tell us something about how far the rules can be bent before they can be broken.”
Questions or comments on this article? E-mail us at [email protected]
A version of this article appears in the March 13, 2021 issue of Science News.
Effects of genotype × environment interactions on plant height traits in soybean
In order to evaluate whether G × E impact plant height in a natural soybean population, 308 representative cultivars from a core soybean germplasm collection  were selected and planted at two distinct experimental sites, Boluo (BL, 114.29°E, 23.17°N) and Hainan (HN, 109.48°E, 18.31°N). Three traits related to plant height (SH, shoot height SNN, stem node number and AIL, average internode length) were determined from field samples. In these tests, the mean values of SH, SNN and AIL were 81.46, 34.05 and 36.24% higher, respectively, in BL than that in HN (P value < 0.001) (Fig. 1a, b and c). This demonstrated that the plant height in soybean significantly varied between two distinct environments. Furthermore, genetic analysis suggested that the distributions for the three tested traits measured in two environments were approximately normal according to Kurtosis and Skewness values calculated over three replicates (Table 1). Broad-sense heritability (h 2 b) for all the traits under the tested environments varied from 0.74 to 0.92, with generally higher values being observed for SH than for the other two traits (Table 1). Regardless of these relatively small differences among traits, the results herein clearly suggest that variation in SH, SNN and AIL depend mainly on genotypic effects in a single environment. Across locations, however, values of h 2 b for SH, SNN and AIL ranged between 0.38 and 0.40, all of which were significantly lower than in individual environments. Taken together, these results strongly suggest that SH, SNN and AIL are all greatly affected by both genotype and environment. In order to further determine G × E, two-way ANOVA was performed. As expected, the results showed that SH, SNN and AIL were significantly all affected (P value < 0.001) by environment, genotype and G × E (Table 2). However, the environment itself consists of many factors, including temperature, day-length, precipitation, soil properties and so on. To sort through these myriad environmental influences, we further evaluated the effects of several primary environmental factors, along with QTLs and QTL × environmental (QTL × E) on the tested traits. Analyzing specific environmental factors in this way might contribute to breeding soybean with shoot architectures optimized for specific sets of environmental conditions.
Plant height traits of soybean varied significantly among geographically distinct growth environments. a-c Plant height traits of 308 soybean cultivars selected from a core germplasm collection and grown in two distinct environments. d-f Plant height traits of 168 F9 recombinant inbred lines (RIL) grown in four environments. HN: Hainan, ZC: Zhao county, HZ: Hangzhou, YZ: Yangzhong, BL: Boluo The black and red lines, lower and upper edges, and bars above or below the boxes represent median and mean values, 25th, 75th, 5th and 95th percentiles of all data, respectively Asterisks and different letters over error bars indicate significant differences of the same trait among different environments in the Student’s t-test at 1‰ (P< 0.001) significance level
Phenotypic variation among recombinant inbred lines
Given the prevalence of G × E identified for soybean in the plant height experiments above, two representative soybean accessions were, therefore, selected for developing a RIL population designed to explore QTL × E more fully in soybean. In addition, field characterizations were performed in an expanded set of four geographically distinct growth environments. In these trials, plant height traits of the parental lines, BX10 with the genotype of E1E2E3E4E9dt1dt2tof11Tof12J and BD2 with the genotype of E1E2E3E4E9Dt1dt2Tof11tof12J, significantly varied across the four tested environments, with observed ranges falling between 33.56 and 122.00 for SH, 9.63 and 23.00 for SNN, and 3.43 and 5.27 for AIL (Table 3). Although there were no significant differences observed between parental lines within individual environments, data from the RIL population exhibited maximum and minimum values beyond the parental extremes, and most of the distributions for traits tested across four environments were approximately normal according to Kurtosis and Skewness values calculated over three replicates (Fig. 2). These results suggest that soybean plant height traits are typical quantitative traits and both parents contain one or more genes contributing additively towards the tested traits. When sites were observed separately, the mean values of SH, SNN and AIL significantly varied in the ranges of 33.20–112.39, 10.07–22.70 and 3.36–5.06, respectively (Fig. 1d, e, f and Table 3), implying large impacts of environmental factors on the tested traits. Furthermore, ANOVA results revealed that the variation observed for SH, SNN and AIL among RILs was significantly affected by environment and genotype, individually or in interaction terms (P value < 0.001) (Table 4). This was consistent with the results obtained from using the core collection germplasm cultivars (Table 2). Overall, the results herein demonstrate that the observed RIL population was suitable for further analysis.
Distributions of plant height traits in 168 F9 RILs reared in four geographically distinct growth environments. Parental values are indicated by red (BX10) and black (BD2) arrows, respectively Skew: Skewness Kurt: Kurtosis SH: shoot height SNN: stem node number AIL: average internode length ZC: Zhao county, HZ: Hangzhou, YZ: Yangzhong, BL: Boluo
Identification of QTLs contributing to plant height traits
A high-density genetic linkage map consisting of 3319 recombinant bin markers had been constructed using the RIL population developed in a previous study . In order to identify significant QTLs, trait mean values were calculated for each RIL line. Subsequent QTL analysis identified a total of 19 significant loci containing 51 QTLs for the three tested traits, with 23, 13 and 15 QTLs being associated with SH, SNN and AIL, respectively. The LOD values of these QTLs ranged from 2.50 to 16.46, and explained 2.80–26.10% of phenotypic variation (Additional file 1: Table S1). Within environments, 13, 16, 13 and 9 QTLs were identified at the Zhao County (ZC, 114.48°E, 37.50°N), Hangzhou (HZ, 120.69°E, 30.51°N), Yangzhong (YZ, 118.20°E, 26.17°N) and BL field sites, respectively. However, only two loci, Loc11 and Loc19–1, containing a total of 20 QTLs, were identified in each of the four distinct environments. Interestingly, the additive effect of Loc11 was derived from BX10 and BD2 as determined in the two southern (including YZ and BL) and two northern (ZC and HZ) experimental stations, respectively. In addition, seven loci (QTLs) were significant only for single trait observed within one of the four tested environments. Other loci contributed to variation in two or more traits and/or at least two environments (Additional file 1: Table S1). The variation in significant QTL numbers and the extent of the additive effects of these QTLs suggests that soybean height QTLs might depend in part on specific environmental conditions present within individual sites, resulting in plant height influenced by genotype, environment, and G × E.
QTL contributions to soybean plant height traits under varied environmental conditions
In order to explore the stability of detected QTL contributions to plant height traits, QTL and plant height data from the four tested environments were subjected to principal components analysis (PCA). In this case, the first two principal components accounted for 44.3 and 25.7% of the total trait variation and QTL additive effects, respectively (Fig. 3a). Traits associated with plant height (SH, SNN and AIL) tended to group together, indicating a high correlation among them. In contrast, the total additive QTL effects for plant height traits (i.e. qSHt, qSNNt and qAILt) tended to group separately, to the extent that nearly 90° angles were observed among the directional vectors (Fig. 3a), which is indicative of these effects acting independently. These results suggest that the detected QTLs do not fully explain the extent of variation in plant height traits observed across varied environments, with the fact that most of these 51 QTLs were not significant in one or more tests reinforcing the conclusion that site specific conditions significantly influenced soybean height outcomes. To test this hypothesis, qSHt, qSNNt and qAILt were replaced by total additive QTL effects (qSHs, qSNNs and qAILs) from the corresponding environments in further PCA. Consistent with the previous PCA results, the first two principal components in this test accounted for 59.2 and 16.8% of the total variation, respectively (Fig. 3b). Besides the vector for qSNNs, the other 5 vectors grouped closely together (Fig. 3b), which suggests, consistent with our hypothesis, that the studied traits are highly correlated. On the other hand, the unexpected PCA results for qSNNs, the vector of which deviated considerably from the vector for SNN, strongly implied that environment differences greatly affected the QTLs for SNN. To minimize environment effects, plant height trait data (SH, SNN and AIL) were replaced by corrected data (SHc, SNNc and AILc) and subjected to PCA again. As expected, the first two principal components accounted for most of the variation, in this case, 42.9 and 24.5% of total variation, respectively (Fig. 3c). Additionally, all three vectors of additive effects (qSHs, qSNNs and qAILs) were relatively close to their corresponding traits (SHc, SNNc and AILc). Taken together, all of the results above strongly indicate that both G × E and QTL × E contribute to plant height phenotypes in the tested soybean population.
Principal component analysis (PCA) among detectable QTLs and soybean plant height traits under varied environments. The PCA plots were drawn based on a the three tested traits and total additive effects of QTLs for each trait b the three tested traits and additive effects of QTLs in single environments, and c additive effects of QTLs in single environments and corrected values for each tested trait SH: shoot height SNN: stem node number AIL: average internode length qSHt, qSNNt and qAILt represent the sum of additive effects of QTLs for SH, SNN and AIL under all environments, respectively qSHs, qSNNs and qAILs represented the sum of additive effects of QTLs for SH, SNN and AIL in single environments, respectively SHc, SNNc and AILc represent corrected values for soybean SH, SNN and AIL, respectively The contributions to phenotypic variation are represented by the color and length of vectors
Genotype × environmental factor interaction effects on plant height traits expressed in RILs
In order to further evaluate the effects of the main environmental factors on soybean plant height traits, correlation analysis and PCA were conducted with data collected for the tested traits, agro-meteorological factors and basic soil chemical properties. Results from PCA clearly showed that the first two principal components accounted for more than 88% of the total variation, and the vectors of AD and AMaT grouped closely with the vectors of SH, AIL and SNN (Fig. 4a). This suggests that both AD and AMaT contribute to enhance SH, SNN and AIL. Although, AMiT, EAT and AT grouped separately from most of the other vectors, their placement below 90°, implies that these three environmental factors might also enhance SH, SNN and AIL (Fig. 4a). This was further supported by the results from Pearson correlation analysis, in which significant correlations were identified among tested traits and agro-meteorological factors and correlation coefficients varied between 0.220–0.827 (P value < 0.01) (Table 5). Contrasting results were obtained when no vectors for soil factors grouped closely with SH, SNN or AIL (Fig. 4b). Except for the angle between pH and AN, all other angles between the AP and AK vectors and plant height traits were larger than 90°, which suggests that there were positive or negative interaction effects of pH and AN, or AP and AK on plant height traits (Fig. 4b). This was further confirmed in Pearson correlation analysis, in which significant positive correlations were established for pH and AN, and negative correlations for AP and AK with SH, SNN and AIL (Table 5). These results strongly demonstrate that both agro-meteorological and soil properties influence plant height traits, but the agro-meteorological factors largely predominate.
Principal component analysis (PCA) plot of relationships among plant height traits, agro-meteorological data and basic soil chemical properties. The PCA plots were drawn based on a the three plant height traits and agro-meteorological data, and b the three plant height traits and basic soil characteristics SH: shoot height SNN: stem node number AIL: average internode length AMaT: average maximum temperature AMiT: average minimum temperature AT: accumulated temperature EAT: effective accumulated temperature AD: average day-length AN: available nitrogen AP: available phosphorus AK: available potassium The contributions to phenotypic variation are represented by the color and lengths of the vectors
QTL × environmental factor interactions in RILs
In order to further explore the main factors imparting QTL additive effects, Pearson correlation analysis and PCA were also performed for agro-meteorological factors, soil properties and QTLs additive effects. Here, AD and AMaT closely grouped with qSHs and qAILs, while, AMiT, EAT and AT distributed separately (Fig. 5a), which is consistent with the relationships obtained in PCA of environmental factors and plant height traits (Fig. 4a). Interestingly, qSNNs aligned very closely with AMiT, yet were far from AMaT, suggesting that the additive effects of qSNNs increased with either increases in AMiT or reductions in AMaT. The positive relationship between qSNNs and AMiT, as well as, the negative relationship between qSNNs and AMaT were further confirmed by correlation analysis, in which the Pearson correlation coefficient was 0.491 between qSNNs and AMiT, or − 0.263 between qSNNs and AMaT (P value < 0.01) (Table 5). Further evaluation of soil properties and plant height traits showed that qSHs were significantly negatively correlated with AP, but positively correlated with pH. Meanwhile, qSNNs exhibited significant negative correlations with AN, and positive correlations with AK, while qAILs had significant positive correlations with two soil factors (pH and AN), but was negatively correlated with AK (Fig. 5b, Table 5). Taken together, these results demonstrate that both agro-meteorological factors and soil properties can significantly affect the additive effects of QTLs in regulating soybean plant height.
Principal component analysis (PCA) plots of relationships among detectable QTLs, agro-meteorological data and basic soil chemical properties. PCA plots were drawn based on relationships between a additive effects of QTLs in single environments and agro-meteorological data, and b additive effects of QTLs in single environments and basic soil characteristics AMaT: average maximum temperature AMiT: average minimum temperature AT: accumulated temperature EAT: effective accumulated temperature AD: average day-length AN: available nitrogen AP: available phosphorus AK: available potassium qSHs, qSNNs and qAILs represent the sum of additive effects of QTLs on soybean shoot height, stem node number and average internode length, respectively, in single environment trials. The contributions to phenotypic variation are represented by the color and lengths of the vectors
The Master of Professional Studies (MPS) in Agriculture and Life Sciences degree offered through the School of Integrative Plant Science (SIPS) is a one-year, course-based master's degree, ideal for individuals who are interested in in-depth study of the issues and advancements in plant and soil sciences.
- Expand our knowledge of plant biology
- Find new ways to produce food for a growing world population
- Breed plants to withstand climate change
- Develop sustainable cropping practices
- Investigate new methods to fight plant disease
- Restore damaged ecosystems
- Conserve species
- Plant Chemistry
- Plant Evolution
- Plants and Humans
- Plant Parts
- Seed Dispersal
As the botanists with UntamedScience add more information this year, we will also add educational videos to these pages. Be patient with us though, these pages are all under construction …
Welcome to the Department of Plant Biology at the University of Georgia.
The Plant Biology department provides an open, inclusive and supportive intellectual environment for students, postdocs, staff and faculty to identify and address cross-cutting fundamental challenges in plant and fungal biology.
We pride ourselves in the breadth and depth of training opportunities in both research and teaching that we provide our graduate students in the Plant Biology program. Our faculty includes internationally recognized researchers advancing knowledge of biological processes acting at all scales, from biochemical and molecular to our global ecosystem. Many of our faculty have interdisciplinary research programs that span multiple traditionally-defined research domains. In addition to plant and fungal biology, our program includes experts in insect evolutionary biology, computational biology, and discipline-based science education, providing our students with a unique breadth of opportunities in their professional training and research.
Developmental Biology and Genetics
The broad fields of Developmental Biology and Genetics are highly interdisciplinary with a presence in all basic medical-science areas, as well as animal and plant biology. Understanding how organisms develop requires a systems-level understanding of how cells achieve different fates, and what combinations of intercellular signaling and intracellular regulatory circuits generate spatially and temporally encoded patterns along the body axis. With the unprecedented expansion of techniques in genomics, molecular biology, and biochemistry, studies in developmental biology require an integrative perspective, applying a "systems" level approach that combines computational and genomic approaches with cell and molecular biology techniques to study developmental phenomena. In particular, many key insights into development are enabled by genetic approaches to understand how genes control the differentiation of cells and the formation of patterns. With regards to our educational mission, we are deeply committed to training outstanding researchers who are well prepared to tackle the exciting challenges that await them in this burgeoning area.
Laboratories that work in this area seek to understand how a multicellular organism arises from a single cell, the fertilized egg. Research in this area spans a broad range of topics, approaches, and experimental systems, including sea urchin development, muscle specification in mice, neural crest development in vertebrates, postembryonic nematode development, Arabidopsis development, mouse T-cell development, Drosophila mesoderm development, Xenopus signaling pathways, stem cell regulatory circuits, genomics and bioinformatics of stem cells, and evolution of development.
Genetics underlies all of biology and much biological inquiry. We build on our rich history in genetics, in which Caltech geneticists such as Morgan, Beadle, Delbruck, Benzer, Wood, Lewis and Hood laid down the foundations of our understanding of genes, gene function, genetic pathways, and genome sequences. Current research on Genetics at Caltech includes modern developmental and behavioral genetics using flies, worms, mice, yeast, Arabidopsis, and zebrafish to elucidate the genetic control of development, physiology, and behavior.
Developmental and Stem Cell Biology
Laboratories working in the area of stem cell biology are focusing on cell lineage decisions in the early embryo and what drives cells from a pluripotent to more restricted state, eventually leading to differentiation into defined cell types. To achieve these goals, we utilize a variety of approaches including high resolution live imaging, lineage tracing, genomic and epigenomic profiling on the whole embryo and single cell level coupled with perturbation approaches.
Over the last two decades, revolutionary improvements in DNA sequencing technology have made it faster, more accurate, and much cheaper. We are now able to sequence up to 10 trillion DNA letters in just one month. I harness these technological advancements to assemble genomes for a variety of organisms and probe the genetic basis of neurological disorders, including autism and schizophrenia, better understand cancer progression and understand the complex structures of the genomes of higher plants.
Genetics problem-Height of a plant - Biology
BIOLOGY 100 - Human Biology
GENETICS PRACTICE PROBLEMS
For each of the diploid genotypes presented below, determine the genetic make up for all of the possible haploid gametes.
Use the Punnett square to determine all of the offspring genotypes (and their relative frequencies) from the following crosses:
In the problem above, the "R" allele is a dominant allele specifying for round seeds (in peas), while the "r" allele is the recessive allele specifying for wrinkled seeds. Give the expected frequencies (as percentages or ratios) for the phenotypes of the offspring resulting from each of the crosses above.
A brown mink crossed with a silverblue mink produced all brown offspring. When these F1 mink were crossed among themselves they produced 47 brown animals and 15 silverblue animals (F2 generation). Determine all the genotypes and phenotypes, and their relative ratios, in the F1 and F2 generations.
In sheep white is due to a dominant gene (W), black to its recessive allele (w). A white ewe mated to a white ram produces a black lamb. If they produce another offspring, could it be white? If so, what are the chances of it being white? List the genotypes of all animals mentioned in this problem.
In tomatoes the texture of the skin may be smooth or peach (hairy). The Ponderosa variety has fruits with smooth texture. The red peach variety has fruits with peach texture. Crosses between the two varieties produce all smooth fruits. Crosses between these smooth fruited F1 plants produced 174 peach textured fruits and 520 smooth textured fruits. How are these skin textures inherited?
A brown mouse is mated is mated with two female black mice. When each female has produced several litters of young, the first female has had 48 black and the second female has had 14 black and 11 brown young. Deduce the pattern of inheritance of coat color and the genotypes of all of the parents.